Overview

Dataset statistics

Number of variables21
Number of observations81426
Missing cells1476
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.6 MiB
Average record size in memory626.2 B

Variable types

Categorical8
Text1
Numeric11
DateTime1

Alerts

regular_price is highly overall correlated with current_price and 3 other fieldsHigh correlation
current_price is highly overall correlated with regular_price and 2 other fieldsHigh correlation
cost is highly overall correlated with regular_price and 3 other fieldsHigh correlation
productgroup is highly overall correlated with regular_price and 3 other fieldsHigh correlation
category is highly overall correlated with regular_price and 2 other fieldsHigh correlation
rgb_r_main_col is highly overall correlated with rgb_g_main_col and 1 other fieldsHigh correlation
rgb_g_main_col is highly overall correlated with rgb_r_main_col and 1 other fieldsHigh correlation
rgb_b_main_col is highly overall correlated with rgb_r_main_col and 1 other fieldsHigh correlation
rgb_r_sec_col is highly overall correlated with rgb_g_sec_col and 1 other fieldsHigh correlation
rgb_g_sec_col is highly overall correlated with rgb_r_sec_col and 1 other fieldsHigh correlation
rgb_b_sec_col is highly overall correlated with rgb_r_sec_col and 1 other fieldsHigh correlation
promo1 is highly imbalanced (65.3%)Imbalance
promo2 is highly imbalanced (95.4%)Imbalance
rgb_r_main_col has 3321 (4.1%) zerosZeros
rgb_b_main_col has 3198 (3.9%) zerosZeros
rgb_r_sec_col has 5166 (6.3%) zerosZeros
rgb_b_sec_col has 3198 (3.9%) zerosZeros

Reproduction

Analysis started2023-08-13 04:09:51.751463
Analysis finished2023-08-13 04:10:49.123714
Duration57.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

country
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
Germany
39975 
Austria
28782 
France
12669 

Length

Max length7
Median length7
Mean length6.8444109
Min length6

Characters and Unicode

Total characters557313
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowGermany
3rd rowGermany
4th rowGermany
5th rowGermany

Common Values

ValueCountFrequency (%)
Germany 39975
49.1%
Austria 28782
35.3%
France 12669
 
15.6%

Length

2023-08-12T23:10:49.336364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T23:10:49.949964image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
germany 39975
49.1%
austria 28782
35.3%
france 12669
 
15.6%

Most occurring characters

ValueCountFrequency (%)
r 81426
14.6%
a 81426
14.6%
e 52644
9.4%
n 52644
9.4%
G 39975
7.2%
m 39975
7.2%
y 39975
7.2%
A 28782
 
5.2%
u 28782
 
5.2%
s 28782
 
5.2%
Other values (4) 82902
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 475887
85.4%
Uppercase Letter 81426
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 81426
17.1%
a 81426
17.1%
e 52644
11.1%
n 52644
11.1%
m 39975
8.4%
y 39975
8.4%
u 28782
 
6.0%
s 28782
 
6.0%
t 28782
 
6.0%
i 28782
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
G 39975
49.1%
A 28782
35.3%
F 12669
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 557313
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 81426
14.6%
a 81426
14.6%
e 52644
9.4%
n 52644
9.4%
G 39975
7.2%
m 39975
7.2%
y 39975
7.2%
A 28782
 
5.2%
u 28782
 
5.2%
s 28782
 
5.2%
Other values (4) 82902
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 557313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 81426
14.6%
a 81426
14.6%
e 52644
9.4%
n 52644
9.4%
G 39975
7.2%
m 39975
7.2%
y 39975
7.2%
A 28782
 
5.2%
u 28782
 
5.2%
s 28782
 
5.2%
Other values (4) 82902
14.9%
Distinct477
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
2023-08-12T23:10:51.086748image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters488556
Distinct characters35
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAA1821
2nd rowAA1821
3rd rowAA1821
4th rowAA1821
5th rowAA1821
ValueCountFrequency (%)
zz2466 369
 
0.5%
mr4948 369
 
0.5%
ez8648 369
 
0.5%
qd9777 369
 
0.5%
ir3275 369
 
0.5%
ze9366 369
 
0.5%
jg1582 369
 
0.5%
zu5523 369
 
0.5%
zv2187 369
 
0.5%
ef6812 369
 
0.5%
Other values (467) 77736
95.5%
2023-08-12T23:10:52.843275image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 39729
 
8.1%
6 38868
 
8.0%
7 38376
 
7.9%
2 37515
 
7.7%
1 35916
 
7.4%
4 35793
 
7.3%
3 34686
 
7.1%
9 33087
 
6.8%
5 31734
 
6.5%
X 8487
 
1.7%
Other values (25) 154365
31.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 325704
66.7%
Uppercase Letter 162852
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 8487
 
5.2%
Z 7995
 
4.9%
F 7872
 
4.8%
R 7626
 
4.7%
V 7257
 
4.5%
B 7011
 
4.3%
A 6642
 
4.1%
M 6642
 
4.1%
T 6519
 
4.0%
L 6519
 
4.0%
Other values (16) 90282
55.4%
Decimal Number
ValueCountFrequency (%)
8 39729
12.2%
6 38868
11.9%
7 38376
11.8%
2 37515
11.5%
1 35916
11.0%
4 35793
11.0%
3 34686
10.6%
9 33087
10.2%
5 31734
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 325704
66.7%
Latin 162852
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 8487
 
5.2%
Z 7995
 
4.9%
F 7872
 
4.8%
R 7626
 
4.7%
V 7257
 
4.5%
B 7011
 
4.3%
A 6642
 
4.1%
M 6642
 
4.1%
T 6519
 
4.0%
L 6519
 
4.0%
Other values (16) 90282
55.4%
Common
ValueCountFrequency (%)
8 39729
12.2%
6 38868
11.9%
7 38376
11.8%
2 37515
11.5%
1 35916
11.0%
4 35793
11.0%
3 34686
10.6%
9 33087
10.2%
5 31734
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 488556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 39729
 
8.1%
6 38868
 
8.0%
7 38376
 
7.9%
2 37515
 
7.7%
1 35916
 
7.4%
4 35793
 
7.3%
3 34686
 
7.1%
9 33087
 
6.8%
5 31734
 
6.5%
X 8487
 
1.7%
Other values (25) 154365
31.6%

sales
Real number (ℝ)

Distinct785
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.048375
Minimum1
Maximum898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:10:53.194856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median26
Q365
95-th percentile218
Maximum898
Range897
Interquartile range (IQR)55

Descriptive statistics

Standard deviation88.506045
Coefficient of variation (CV)1.551421
Kurtosis20.153341
Mean57.048375
Median Absolute Deviation (MAD)20
Skewness3.8345665
Sum4645221
Variance7833.32
MonotonicityNot monotonic
2023-08-12T23:10:53.498755image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2422
 
3.0%
3 2342
 
2.9%
2 2336
 
2.9%
4 2262
 
2.8%
5 2197
 
2.7%
6 2065
 
2.5%
7 2035
 
2.5%
8 1897
 
2.3%
9 1832
 
2.2%
10 1745
 
2.1%
Other values (775) 60293
74.0%
ValueCountFrequency (%)
1 2422
3.0%
2 2336
2.9%
3 2342
2.9%
4 2262
2.8%
5 2197
2.7%
6 2065
2.5%
7 2035
2.5%
8 1897
2.3%
9 1832
2.2%
10 1745
2.1%
ValueCountFrequency (%)
898 1
< 0.1%
897 1
< 0.1%
896 1
< 0.1%
895 1
< 0.1%
892 1
< 0.1%
891 2
< 0.1%
889 2
< 0.1%
888 1
< 0.1%
887 1
< 0.1%
884 1
< 0.1%

regular_price
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.620695
Minimum3.95
Maximum197.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:10:53.804894image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.95
5-th percentile6.95
Q125.95
median41.45
Q379.95
95-th percentile120.95
Maximum197.95
Range194
Interquartile range (IQR)54

Descriptive statistics

Standard deviation35.549601
Coefficient of variation (CV)0.67558213
Kurtosis0.34446831
Mean52.620695
Median Absolute Deviation (MAD)20.5
Skewness0.9060747
Sum4284692.7
Variance1263.7741
MonotonicityNot monotonic
2023-08-12T23:10:54.642071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.95 2829
 
3.5%
26.95 2706
 
3.3%
29.95 2460
 
3.0%
23.95 2460
 
3.0%
62.95 2091
 
2.6%
25.95 2091
 
2.6%
44.95 1968
 
2.4%
20.95 1845
 
2.3%
3.95 1845
 
2.3%
13.95 1599
 
2.0%
Other values (113) 59532
73.1%
ValueCountFrequency (%)
3.95 1845
2.3%
4.95 492
 
0.6%
5.95 984
1.2%
6.95 1107
1.4%
7.95 123
 
0.2%
8.95 492
 
0.6%
9.95 492
 
0.6%
10.95 615
 
0.8%
11.95 123
 
0.2%
12.95 123
 
0.2%
ValueCountFrequency (%)
197.95 123
 
0.2%
195.95 123
 
0.2%
153.95 738
0.9%
150.95 123
 
0.2%
141.95 123
 
0.2%
139.95 246
 
0.3%
136.95 123
 
0.2%
135.95 246
 
0.3%
134.95 123
 
0.2%
132.95 369
0.5%

current_price
Real number (ℝ)

HIGH CORRELATION 

Distinct174
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.356811
Minimum1.95
Maximum197.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:10:55.352376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.95
5-th percentile3.95
Q111.95
median20.95
Q337.95
95-th percentile74.95
Maximum197.95
Range196
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.704746
Coefficient of variation (CV)0.80068052
Kurtosis2.8666981
Mean28.356811
Median Absolute Deviation (MAD)11
Skewness1.5427149
Sum2308981.7
Variance515.5055
MonotonicityNot monotonic
2023-08-12T23:10:55.639300image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.95 2981
 
3.7%
9.95 2673
 
3.3%
12.95 2410
 
3.0%
11.95 2389
 
2.9%
7.95 2305
 
2.8%
16.95 2291
 
2.8%
13.95 2275
 
2.8%
10.95 2223
 
2.7%
17.95 2179
 
2.7%
14.95 2150
 
2.6%
Other values (164) 57550
70.7%
ValueCountFrequency (%)
1.95 1335
1.6%
2.95 1732
2.1%
3.95 1222
1.5%
4.95 1467
1.8%
5.95 1474
1.8%
6.95 1721
2.1%
7.95 2305
2.8%
8.95 2981
3.7%
9.95 2673
3.3%
10.95 2223
2.7%
ValueCountFrequency (%)
197.95 1
< 0.1%
195.95 2
< 0.1%
192.95 1
< 0.1%
188.95 1
< 0.1%
184.95 2
< 0.1%
179.95 1
< 0.1%
178.95 2
< 0.1%
174.95 1
< 0.1%
172.95 1
< 0.1%
171.95 2
< 0.1%

ratio
Real number (ℝ)

Distinct5154
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54446396
Minimum0.29648241
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:10:55.956271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.29648241
5-th percentile0.30294906
Q10.35483871
median0.52352591
Q30.69732247
95-th percentile0.8820059
Maximum1
Range0.70351759
Interquartile range (IQR)0.34248376

Descriptive statistics

Standard deviation0.19269181
Coefficient of variation (CV)0.35391105
Kurtosis-0.88897153
Mean0.54446396
Median Absolute Deviation (MAD)0.1686872
Skewness0.40543976
Sum44333.523
Variance0.037130135
MonotonicityNot monotonic
2023-08-12T23:10:56.267004image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1232
 
1.5%
0.4936708861 931
 
1.1%
0.746835443 705
 
0.9%
0.3214862682 689
 
0.8%
0.3103448276 660
 
0.8%
0.332096475 651
 
0.8%
0.3319415449 628
 
0.8%
0.3548387097 517
 
0.6%
0.2988313856 509
 
0.6%
0.3317422434 473
 
0.6%
Other values (5144) 74431
91.4%
ValueCountFrequency (%)
0.2964824121 115
 
0.1%
0.298245614 171
 
0.2%
0.2988313856 509
0.6%
0.2991239049 158
 
0.2%
0.2992992993 119
 
0.1%
0.2994161802 31
 
< 0.1%
0.2994996426 185
 
0.2%
0.2995622264 42
 
0.1%
0.2996108949 59
 
0.1%
0.2996498249 62
 
0.1%
ValueCountFrequency (%)
1 1232
1.5%
0.9935043845 1
 
< 0.1%
0.9929552659 1
 
< 0.1%
0.9928545909 1
 
< 0.1%
0.9924783753 1
 
< 0.1%
0.9920603414 3
 
< 0.1%
0.9917321207 1
 
< 0.1%
0.9915218313 1
 
< 0.1%
0.9913005655 1
 
< 0.1%
0.9904716532 3
 
< 0.1%
Distinct123
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size636.3 KiB
Minimum2014-12-28 00:00:00
Maximum2017-04-30 00:00:00
2023-08-12T23:10:56.552909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:56.822991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

promo1
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
76130 
1
 
5296

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters81426
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 76130
93.5%
1 5296
 
6.5%

Length

2023-08-12T23:10:57.122044image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T23:10:57.631152image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 76130
93.5%
1 5296
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 76130
93.5%
1 5296
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81426
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76130
93.5%
1 5296
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 81426
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76130
93.5%
1 5296
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76130
93.5%
1 5296
 
6.5%

promo2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
0
81014 
1
 
412

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters81426
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 81014
99.5%
1 412
 
0.5%

Length

2023-08-12T23:10:57.839339image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T23:10:58.116630image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 81014
99.5%
1 412
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 81014
99.5%
1 412
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81426
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 81014
99.5%
1 412
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 81426
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 81014
99.5%
1 412
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 81014
99.5%
1 412
 
0.5%

productgroup
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing123
Missing (%)0.2%
Memory size5.1 MiB
SHOES
32964 
HARDWARE ACCESSORIES
15990 
SHORTS
13284 
T-SHIRTS
7872 
PANTS
6273 

Length

Max length20
Median length11
Mean length8.7670197
Min length5

Characters and Unicode

Total characters712785
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT-SHIRTS
2nd rowT-SHIRTS
3rd rowT-SHIRTS
4th rowT-SHIRTS
5th rowT-SHIRTS

Common Values

ValueCountFrequency (%)
SHOES 32964
40.5%
HARDWARE ACCESSORIES 15990
19.6%
SHORTS 13284
16.3%
T-SHIRTS 7872
 
9.7%
PANTS 6273
 
7.7%
SWEATSHIRTS 4920
 
6.0%
(Missing) 123
 
0.2%

Length

2023-08-12T23:10:58.317224image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T23:10:58.599522image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
shoes 32964
33.9%
hardware 15990
16.4%
accessories 15990
16.4%
shorts 13284
13.7%
t-shirts 7872
 
8.1%
pants 6273
 
6.4%
sweatshirts 4920
 
5.1%

Most occurring characters

ValueCountFrequency (%)
S 177243
24.9%
E 85854
12.0%
H 75030
10.5%
R 74046
10.4%
O 62238
 
8.7%
A 59163
 
8.3%
T 45141
 
6.3%
C 31980
 
4.5%
I 28782
 
4.0%
W 20910
 
2.9%
Other values (5) 52398
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 688923
96.7%
Space Separator 15990
 
2.2%
Dash Punctuation 7872
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 177243
25.7%
E 85854
12.5%
H 75030
10.9%
R 74046
10.7%
O 62238
 
9.0%
A 59163
 
8.6%
T 45141
 
6.6%
C 31980
 
4.6%
I 28782
 
4.2%
W 20910
 
3.0%
Other values (3) 28536
 
4.1%
Space Separator
ValueCountFrequency (%)
15990
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7872
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 688923
96.7%
Common 23862
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 177243
25.7%
E 85854
12.5%
H 75030
10.9%
R 74046
10.7%
O 62238
 
9.0%
A 59163
 
8.6%
T 45141
 
6.6%
C 31980
 
4.6%
I 28782
 
4.2%
W 20910
 
3.0%
Other values (3) 28536
 
4.1%
Common
ValueCountFrequency (%)
15990
67.0%
- 7872
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 712785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 177243
24.9%
E 85854
12.0%
H 75030
10.5%
R 74046
10.4%
O 62238
 
8.7%
A 59163
 
8.3%
T 45141
 
6.3%
C 31980
 
4.5%
I 28782
 
4.0%
W 20910
 
2.9%
Other values (5) 52398
 
7.4%

category
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)< 0.1%
Missing123
Missing (%)0.2%
Memory size5.1 MiB
TRAINING
26937 
RUNNING
17712 
FOOTBALL GENERIC
10947 
FOOTBALL LICENSED
5781 
OUTDOOR
5658 
Other values (16)
14268 

Length

Max length18
Median length17
Mean length9.1815431
Min length3

Characters and Unicode

Total characters746487
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRUNNING
2nd rowRUNNING
3rd rowRUNNING
4th rowRUNNING
5th rowRUNNING

Common Values

ValueCountFrequency (%)
TRAINING 26937
33.1%
RUNNING 17712
21.8%
FOOTBALL GENERIC 10947
13.4%
FOOTBALL LICENSED 5781
 
7.1%
OUTDOOR 5658
 
6.9%
TENNIS 3198
 
3.9%
INDOOR 2952
 
3.6%
SWIM 2706
 
3.3%
GOLF 1722
 
2.1%
ORIGINALS 615
 
0.8%
Other values (11) 3075
 
3.8%

Length

2023-08-12T23:10:58.889428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
training 26937
26.9%
running 17712
17.7%
football 16728
16.7%
generic 11562
11.6%
licensed 5781
 
5.8%
outdoor 5658
 
5.7%
tennis 3198
 
3.2%
indoor 2952
 
3.0%
swim 2706
 
2.7%
golf 1722
 
1.7%
Other values (14) 5043
 
5.0%

Most occurring characters

ValueCountFrequency (%)
N 134931
18.1%
I 99753
13.4%
R 66912
9.0%
O 59901
8.0%
G 58548
7.8%
T 53997
7.2%
A 49077
 
6.6%
L 45141
 
6.0%
E 40344
 
5.4%
U 24477
 
3.3%
Other values (14) 113406
15.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 727791
97.5%
Space Separator 18696
 
2.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 134931
18.5%
I 99753
13.7%
R 66912
9.2%
O 59901
8.2%
G 58548
8.0%
T 53997
7.4%
A 49077
 
6.7%
L 45141
 
6.2%
E 40344
 
5.5%
U 24477
 
3.4%
Other values (13) 94710
13.0%
Space Separator
ValueCountFrequency (%)
18696
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 727791
97.5%
Common 18696
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 134931
18.5%
I 99753
13.7%
R 66912
9.2%
O 59901
8.2%
G 58548
8.0%
T 53997
7.4%
A 49077
 
6.7%
L 45141
 
6.2%
E 40344
 
5.5%
U 24477
 
3.4%
Other values (13) 94710
13.0%
Common
ValueCountFrequency (%)
18696
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 746487
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 134931
18.1%
I 99753
13.4%
R 66912
9.0%
O 59901
8.0%
G 58548
7.8%
T 53997
7.2%
A 49077
 
6.6%
L 45141
 
6.0%
E 40344
 
5.4%
U 24477
 
3.3%
Other values (14) 113406
15.2%

cost
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)0.1%
Missing123
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.0022542
Minimum0.4
Maximum19.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:10:59.166260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.7
Q12.4
median4
Q37.7
95-th percentile11.3
Maximum19.8
Range19.4
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation3.3522163
Coefficient of variation (CV)0.67014114
Kurtosis0.091648775
Mean5.0022542
Median Absolute Deviation (MAD)1.9
Skewness0.84216321
Sum406698.27
Variance11.237354
MonotonicityNot monotonic
2023-08-12T23:10:59.439586image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4 2583
 
3.2%
2.7 2460
 
3.0%
3 2460
 
3.0%
3.1 2460
 
3.0%
2.1 2337
 
2.9%
6.3 2091
 
2.6%
2.29 1968
 
2.4%
2.9 1968
 
2.4%
0.4 1845
 
2.3%
2.6 1722
 
2.1%
Other values (108) 59409
73.0%
ValueCountFrequency (%)
0.4 1845
2.3%
0.5 492
 
0.6%
0.6 1230
1.5%
0.7 1107
1.4%
0.9 492
 
0.6%
1 615
 
0.8%
1.09 369
 
0.5%
1.2 123
 
0.2%
1.29 123
 
0.2%
1.4 1722
2.1%
ValueCountFrequency (%)
19.8 123
 
0.2%
15.39 246
0.3%
15.09 123
 
0.2%
13.59 492
0.6%
13.49 246
0.3%
13.29 246
0.3%
13.09 123
 
0.2%
12.89 123
 
0.2%
12.6 246
0.3%
12.3 123
 
0.2%

style
Categorical

Distinct3
Distinct (%)< 0.1%
Missing123
Missing (%)0.2%
Memory size4.8 MiB
slim
29643 
regular
26937 
wide
24723 

Length

Max length7
Median length4
Mean length4.9939486
Min length4

Characters and Unicode

Total characters406023
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwide
2nd rowwide
3rd rowwide
4th rowwide
5th rowwide

Common Values

ValueCountFrequency (%)
slim 29643
36.4%
regular 26937
33.1%
wide 24723
30.4%
(Missing) 123
 
0.2%

Length

2023-08-12T23:10:59.694852image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T23:10:59.946550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
slim 29643
36.5%
regular 26937
33.1%
wide 24723
30.4%

Most occurring characters

ValueCountFrequency (%)
l 56580
13.9%
i 54366
13.4%
r 53874
13.3%
e 51660
12.7%
s 29643
7.3%
m 29643
7.3%
g 26937
6.6%
u 26937
6.6%
a 26937
6.6%
w 24723
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 406023
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 56580
13.9%
i 54366
13.4%
r 53874
13.3%
e 51660
12.7%
s 29643
7.3%
m 29643
7.3%
g 26937
6.6%
u 26937
6.6%
a 26937
6.6%
w 24723
6.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 406023
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 56580
13.9%
i 54366
13.4%
r 53874
13.3%
e 51660
12.7%
s 29643
7.3%
m 29643
7.3%
g 26937
6.6%
u 26937
6.6%
a 26937
6.6%
w 24723
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 406023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 56580
13.9%
i 54366
13.4%
r 53874
13.3%
e 51660
12.7%
s 29643
7.3%
m 29643
7.3%
g 26937
6.6%
u 26937
6.6%
a 26937
6.6%
w 24723
6.1%

sizes
Categorical

Distinct8
Distinct (%)< 0.1%
Missing123
Missing (%)0.2%
Memory size5.7 MiB
xxs,xs,s,m,l,xl,xxl
49200 
xs,s,m,l,xl
6765 
xs,s,m,l
 
4674
s,m,l,xl
 
4674
s,m,l,xl,xxl
 
4059
Other values (3)
11931 

Length

Max length19
Median length19
Mean length15.992436
Min length8

Characters and Unicode

Total characters1300233
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rows,m,l,xl,xxl
2nd rows,m,l,xl,xxl
3rd rows,m,l,xl,xxl
4th rows,m,l,xl,xxl
5th rows,m,l,xl,xxl

Common Values

ValueCountFrequency (%)
xxs,xs,s,m,l,xl,xxl 49200
60.4%
xs,s,m,l,xl 6765
 
8.3%
xs,s,m,l 4674
 
5.7%
s,m,l,xl 4674
 
5.7%
s,m,l,xl,xxl 4059
 
5.0%
xs,s,m,l,xl,xxl 4059
 
5.0%
xxs,xs,s,m,l,xl 4059
 
5.0%
xxs,xs,s,m,l 3813
 
4.7%
(Missing) 123
 
0.2%

Length

2023-08-12T23:11:00.159572image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T23:11:00.446512image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
xxs,xs,s,m,l,xl,xxl 49200
60.5%
xs,s,m,l,xl 6765
 
8.3%
xs,s,m,l 4674
 
5.7%
s,m,l,xl 4674
 
5.7%
s,m,l,xl,xxl 4059
 
5.0%
xs,s,m,l,xl,xxl 4059
 
5.0%
xxs,xs,s,m,l,xl 4059
 
5.0%
xxs,xs,s,m,l 3813
 
4.7%

Most occurring characters

ValueCountFrequency (%)
, 422382
32.5%
x 374166
28.8%
l 211437
16.3%
s 210945
16.2%
m 81303
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 877851
67.5%
Other Punctuation 422382
32.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
x 374166
42.6%
l 211437
24.1%
s 210945
24.0%
m 81303
 
9.3%
Other Punctuation
ValueCountFrequency (%)
, 422382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 877851
67.5%
Common 422382
32.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
x 374166
42.6%
l 211437
24.1%
s 210945
24.0%
m 81303
 
9.3%
Common
ValueCountFrequency (%)
, 422382
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1300233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 422382
32.5%
x 374166
28.8%
l 211437
16.3%
s 210945
16.2%
m 81303
 
6.3%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing123
Missing (%)0.2%
Memory size4.8 MiB
women
43419 
kids
14145 
unisex
11931 
men
11808 

Length

Max length6
Median length5
Mean length4.6822995
Min length3

Characters and Unicode

Total characters380685
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunisex
2nd rowunisex
3rd rowunisex
4th rowunisex
5th rowunisex

Common Values

ValueCountFrequency (%)
women 43419
53.3%
kids 14145
 
17.4%
unisex 11931
 
14.7%
men 11808
 
14.5%
(Missing) 123
 
0.2%

Length

2023-08-12T23:11:00.705841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-12T23:11:00.974726image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
women 43419
53.4%
kids 14145
 
17.4%
unisex 11931
 
14.7%
men 11808
 
14.5%

Most occurring characters

ValueCountFrequency (%)
e 67158
17.6%
n 67158
17.6%
m 55227
14.5%
w 43419
11.4%
o 43419
11.4%
i 26076
 
6.8%
s 26076
 
6.8%
k 14145
 
3.7%
d 14145
 
3.7%
u 11931
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 380685
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 67158
17.6%
n 67158
17.6%
m 55227
14.5%
w 43419
11.4%
o 43419
11.4%
i 26076
 
6.8%
s 26076
 
6.8%
k 14145
 
3.7%
d 14145
 
3.7%
u 11931
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 380685
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 67158
17.6%
n 67158
17.6%
m 55227
14.5%
w 43419
11.4%
o 43419
11.4%
i 26076
 
6.8%
s 26076
 
6.8%
k 14145
 
3.7%
d 14145
 
3.7%
u 11931
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 380685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 67158
17.6%
n 67158
17.6%
m 55227
14.5%
w 43419
11.4%
o 43419
11.4%
i 26076
 
6.8%
s 26076
 
6.8%
k 14145
 
3.7%
d 14145
 
3.7%
u 11931
 
3.1%

rgb_r_main_col
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)0.1%
Missing123
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean162.31467
Minimum0
Maximum255
Zeros3321
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:11:01.216494image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1127
median179
Q3214
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)87

Descriptive statistics

Standard deviation72.027633
Coefficient of variation (CV)0.44375306
Kurtosis-0.5814479
Mean162.31467
Median Absolute Deviation (MAD)44
Skewness-0.60990774
Sum13196670
Variance5187.9799
MonotonicityNot monotonic
2023-08-12T23:11:01.488644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
205 13038
 
16.0%
255 7503
 
9.2%
139 6642
 
8.2%
238 6396
 
7.9%
0 3321
 
4.1%
135 1968
 
2.4%
79 1968
 
2.4%
50 1845
 
2.3%
250 1722
 
2.1%
54 1722
 
2.1%
Other values (39) 35178
43.2%
ValueCountFrequency (%)
0 3321
4.1%
13 615
 
0.8%
24 1230
 
1.5%
36 861
 
1.1%
50 1845
2.3%
54 1722
2.1%
58 615
 
0.8%
69 369
 
0.5%
72 861
 
1.1%
74 861
 
1.1%
ValueCountFrequency (%)
255 7503
9.2%
250 1722
 
2.1%
248 1107
 
1.4%
245 369
 
0.5%
240 492
 
0.6%
238 6396
7.9%
220 861
 
1.1%
218 1599
 
2.0%
214 1230
 
1.5%
209 984
 
1.2%

rgb_g_main_col
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)0.1%
Missing123
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean156.00454
Minimum10
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:11:01.755202image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile36
Q1121
median155
Q3205
95-th percentile245
Maximum255
Range245
Interquartile range (IQR)84

Descriptive statistics

Standard deviation60.315943
Coefficient of variation (CV)0.38662941
Kurtosis-0.30586762
Mean156.00454
Median Absolute Deviation (MAD)42
Skewness-0.45773936
Sum12683637
Variance3638.013
MonotonicityNot monotonic
2023-08-12T23:11:02.019591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
238 4182
 
5.1%
197 3813
 
4.7%
205 3690
 
4.5%
140 2829
 
3.5%
112 2091
 
2.6%
139 2091
 
2.6%
155 1968
 
2.4%
181 1845
 
2.3%
173 1845
 
2.3%
158 1722
 
2.1%
Other values (65) 55227
67.8%
ValueCountFrequency (%)
10 1107
1.4%
13 615
0.8%
16 861
1.1%
26 738
0.9%
36 861
1.1%
41 492
0.6%
43 861
1.1%
44 492
0.6%
51 738
0.9%
54 738
0.9%
ValueCountFrequency (%)
255 1353
 
1.7%
250 984
 
1.2%
248 1107
 
1.4%
245 1599
 
2.0%
240 246
 
0.3%
238 4182
5.1%
235 861
 
1.1%
233 492
 
0.6%
230 615
 
0.8%
229 369
 
0.5%

rgb_b_main_col
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.1%
Missing123
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean142.20424
Minimum0
Maximum255
Zeros3198
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:11:02.274539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q184
median143
Q3205
95-th percentile250
Maximum255
Range255
Interquartile range (IQR)121

Descriptive statistics

Standard deviation71.429524
Coefficient of variation (CV)0.50230236
Kurtosis-0.93128446
Mean142.20424
Median Absolute Deviation (MAD)62
Skewness-0.24243245
Sum11561631
Variance5102.1769
MonotonicityNot monotonic
2023-08-12T23:11:02.539067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 8487
 
10.4%
255 3813
 
4.7%
238 3690
 
4.5%
139 3567
 
4.4%
0 3198
 
3.9%
145 2583
 
3.2%
214 2460
 
3.0%
50 2337
 
2.9%
115 2214
 
2.7%
158 1968
 
2.4%
Other values (55) 46986
57.7%
ValueCountFrequency (%)
0 3198
3.9%
8 615
 
0.8%
13 615
 
0.8%
26 738
 
0.9%
29 1107
 
1.4%
32 369
 
0.5%
34 615
 
0.8%
36 861
 
1.1%
50 2337
2.9%
51 738
 
0.9%
ValueCountFrequency (%)
255 3813
4.7%
250 738
 
0.9%
245 615
 
0.8%
240 1353
 
1.7%
238 3690
4.5%
226 861
 
1.1%
222 369
 
0.5%
220 861
 
1.1%
215 861
 
1.1%
214 2460
3.0%

rgb_r_sec_col
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)0.1%
Missing123
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean159.32829
Minimum0
Maximum255
Zeros5166
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:11:02.822726image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1115
median174
Q3220
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)105

Descriptive statistics

Standard deviation76.960606
Coefficient of variation (CV)0.48303164
Kurtosis-0.68944452
Mean159.32829
Median Absolute Deviation (MAD)47
Skewness-0.61048839
Sum12953868
Variance5922.9348
MonotonicityNot monotonic
2023-08-12T23:11:03.169098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
205 11562
 
14.2%
255 7749
 
9.5%
238 6888
 
8.5%
139 6150
 
7.6%
0 5166
 
6.3%
79 2337
 
2.9%
54 2091
 
2.6%
135 1845
 
2.3%
154 1845
 
2.3%
188 1599
 
2.0%
Other values (39) 34071
41.8%
ValueCountFrequency (%)
0 5166
6.3%
13 1476
 
1.8%
24 738
 
0.9%
36 1107
 
1.4%
50 1230
 
1.5%
54 2091
2.6%
58 615
 
0.8%
69 1107
 
1.4%
72 369
 
0.5%
74 369
 
0.5%
ValueCountFrequency (%)
255 7749
9.5%
250 1599
 
2.0%
248 861
 
1.1%
245 1353
 
1.7%
240 1107
 
1.4%
238 6888
8.5%
220 1107
 
1.4%
218 1353
 
1.7%
214 246
 
0.3%
209 1230
 
1.5%

rgb_g_sec_col
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)0.1%
Missing123
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean156.91377
Minimum10
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:11:03.426252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile41
Q1127
median154
Q3205
95-th percentile245
Maximum255
Range245
Interquartile range (IQR)78

Descriptive statistics

Standard deviation62.019242
Coefficient of variation (CV)0.39524411
Kurtosis-0.47098506
Mean156.91377
Median Absolute Deviation (MAD)47
Skewness-0.4287058
Sum12757560
Variance3846.3863
MonotonicityNot monotonic
2023-08-12T23:11:03.696509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 5904
 
7.3%
238 4305
 
5.3%
245 3444
 
4.2%
140 3198
 
3.9%
180 2829
 
3.5%
139 2829
 
3.5%
181 1722
 
2.1%
128 1722
 
2.1%
104 1599
 
2.0%
255 1476
 
1.8%
Other values (65) 52275
64.2%
ValueCountFrequency (%)
10 246
 
0.3%
13 1476
1.8%
16 615
0.8%
26 615
0.8%
36 1107
1.4%
41 246
 
0.3%
43 1353
1.7%
44 369
 
0.5%
51 984
1.2%
54 1230
1.5%
ValueCountFrequency (%)
255 1476
 
1.8%
250 492
 
0.6%
248 861
 
1.1%
245 3444
4.2%
240 738
 
0.9%
238 4305
5.3%
235 984
 
1.2%
233 369
 
0.5%
230 984
 
1.2%
229 492
 
0.6%

rgb_b_sec_col
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.1%
Missing123
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean139.6354
Minimum0
Maximum255
Zeros3198
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size636.3 KiB
2023-08-12T23:11:03.958345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q179
median139
Q3205
95-th percentile245
Maximum255
Range255
Interquartile range (IQR)126

Descriptive statistics

Standard deviation74.344397
Coefficient of variation (CV)0.53241797
Kurtosis-1.0635615
Mean139.6354
Median Absolute Deviation (MAD)66
Skewness-0.20349102
Sum11352777
Variance5527.0894
MonotonicityNot monotonic
2023-08-12T23:11:04.223317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 9102
 
11.2%
238 3690
 
4.5%
139 3444
 
4.2%
0 3198
 
3.9%
50 3075
 
3.8%
255 2829
 
3.5%
158 2214
 
2.7%
245 2091
 
2.6%
145 1722
 
2.1%
180 1722
 
2.1%
Other values (55) 48216
59.2%
ValueCountFrequency (%)
0 3198
3.9%
8 738
 
0.9%
13 1476
1.8%
26 615
 
0.8%
29 615
 
0.8%
32 861
 
1.1%
34 1599
2.0%
36 1107
 
1.4%
50 3075
3.8%
51 984
 
1.2%
ValueCountFrequency (%)
255 2829
3.5%
250 1107
 
1.4%
245 2091
2.6%
240 1476
 
1.8%
238 3690
4.5%
226 1353
 
1.7%
222 492
 
0.6%
220 1107
 
1.4%
215 984
 
1.2%
214 738
 
0.9%

Interactions

2023-08-12T23:10:43.614742image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:13.872331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:16.662728image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:19.666087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:23.964072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:26.541987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:30.205229image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:32.847574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:35.267185image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:37.645220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:40.884208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:43.815896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:14.148892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:16.873247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:20.108485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:24.239550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:26.775555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:30.459374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:33.046237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:35.480539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:37.836122image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:41.245116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:44.039745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:14.440277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:17.104308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:20.380788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:24.502909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:27.091904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:30.758994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:33.330353image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:35.699567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:38.054450image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:41.521273image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:44.256928image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:14.778677image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:17.421977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:20.644116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:24.740775image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:27.472051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:30.999091image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:33.566455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:35.920526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:38.286373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:41.804985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:44.489452image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:15.186833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:17.677297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:21.023375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:24.995474image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:27.942960image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:31.252387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:33.788692image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:36.145909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:38.914709image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:42.024390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:44.726192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:15.426794image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:18.078500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:21.277923image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:25.247547image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:28.286170image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:31.510832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:34.009537image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:36.377409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:39.268838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:42.251108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:44.928349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:15.624985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:18.291589image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:21.504204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:25.478136image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:28.604230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:31.720991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:34.217160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:36.587739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:39.575335image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:42.484691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:45.136802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:15.844515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:18.515423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:21.734481image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:25.687546image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:28.899719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:31.935017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:34.419115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:36.785595image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:39.882797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:42.771409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:45.340547image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:16.045618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:18.758900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:22.360573image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:25.897005image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:29.324566image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:32.168768image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:34.648779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:36.996671image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:40.158108image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:42.982405image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:45.551803image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:16.249337image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:18.968116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:22.933729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:26.104008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:29.591135image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:32.366994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:34.865767image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:37.191593image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:40.415845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:43.209346image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:46.051572image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:16.451439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:19.203137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:23.390483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:26.323458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:29.875609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:32.647071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:35.066555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:37.389984image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:40.678479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-08-12T23:10:43.416953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-08-12T23:11:04.461476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
salesregular_pricecurrent_priceratiocostrgb_r_main_colrgb_g_main_colrgb_b_main_colrgb_r_sec_colrgb_g_sec_colrgb_b_sec_colcountrypromo1promo2productgroupcategorystylesizesgender
sales1.0000.037-0.146-0.4250.045-0.003-0.0200.001-0.018-0.009-0.0070.0090.0990.0180.0220.0410.0000.0120.013
regular_price0.0371.0000.889-0.0690.9590.002-0.0040.079-0.045-0.010-0.0300.1700.0000.0330.4770.3920.0900.1190.109
current_price-0.1460.8891.0000.3630.8540.002-0.0100.072-0.038-0.011-0.0300.0730.0660.0380.3180.2000.0310.0500.058
ratio-0.425-0.0690.3631.000-0.0690.0030.002-0.0030.0010.002-0.0020.0170.1630.0310.0440.0530.0160.0150.022
cost0.0450.9590.854-0.0691.0000.0050.0140.088-0.046-0.012-0.0490.1370.0000.0240.4770.4470.0960.1320.130
rgb_r_main_col-0.0030.0020.0020.0030.0051.0000.4050.2210.0850.046-0.0660.0950.0000.0170.1460.1840.1250.1420.193
rgb_g_main_col-0.020-0.004-0.0100.0020.0140.4051.0000.3960.0500.016-0.0430.1010.0000.0130.1310.1920.1490.1530.160
rgb_b_main_col0.0010.0790.072-0.0030.0880.2210.3961.0000.068-0.017-0.0050.1380.0000.0180.1540.1880.1330.1520.126
rgb_r_sec_col-0.018-0.045-0.0380.001-0.0460.0850.0500.0681.0000.4180.2170.0680.0000.0040.1380.1960.1680.1530.161
rgb_g_sec_col-0.009-0.010-0.0110.002-0.0120.0460.016-0.0170.4181.0000.4610.0910.0000.0150.1360.1790.1340.1440.141
rgb_b_sec_col-0.007-0.030-0.030-0.002-0.049-0.066-0.043-0.0050.2170.4611.0000.0790.0000.0110.1490.1880.1590.1370.119
country0.0090.1700.0730.0170.1370.0950.1010.1380.0680.0910.0791.0000.0000.1660.1330.2400.0420.0910.053
promo10.0990.0000.0660.1630.0000.0000.0000.0000.0000.0000.0000.0001.0000.0530.0000.0000.0000.0000.000
promo20.0180.0330.0380.0310.0240.0170.0130.0180.0040.0150.0110.1660.0531.0000.0250.0420.0060.0110.009
productgroup0.0220.4770.3180.0440.4770.1460.1310.1540.1380.1360.1490.1330.0000.0251.0000.3600.1020.1340.114
category0.0410.3920.2000.0530.4470.1840.1920.1880.1960.1790.1880.2400.0000.0420.3601.0000.1730.1820.163
style0.0000.0900.0310.0160.0960.1250.1490.1330.1680.1340.1590.0420.0000.0060.1020.1731.0000.1430.097
sizes0.0120.1190.0500.0150.1320.1420.1530.1520.1530.1440.1370.0910.0000.0110.1340.1820.1431.0000.149
gender0.0130.1090.0580.0220.1300.1930.1600.1260.1610.1410.1190.0530.0000.0090.1140.1630.0970.1491.000
2023-08-12T23:11:04.886198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
countrysalesregular_pricecurrent_priceratiopromo1promo2productgroupcategorycoststylesizesgenderrgb_r_main_colrgb_g_main_colrgb_b_main_colrgb_r_sec_colrgb_g_sec_colrgb_b_sec_col
country1.0000.0150.3610.1220.0280.0000.1000.3070.4590.3000.1390.1430.0560.1580.1680.2320.1150.1520.130
sales0.0151.0000.0320.0990.2470.1290.0240.0410.1120.0420.0000.0250.0220.0440.0250.0550.0380.0300.038
regular_price0.3610.0321.0000.6800.0840.0000.0340.7380.7340.9880.2070.2270.1690.2330.2760.2770.2730.2700.276
current_price0.1220.0990.6801.0000.4610.0860.0500.5360.4840.6260.0530.1050.0970.1400.1800.1640.1540.1900.142
ratio0.0280.2470.0840.4611.0000.2120.0410.0840.1430.0780.0280.0320.0370.0710.0510.0800.0730.0670.075
promo10.0000.1290.0000.0860.2121.0000.0830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
promo20.1000.0240.0340.0500.0410.0831.0000.0350.0480.0240.0030.0150.0130.0220.0160.0260.0050.0190.013
productgroup0.3070.0410.7380.5360.0840.0000.0351.0000.6510.7380.2400.2380.1760.2710.2440.2670.2570.2540.283
category0.4590.1120.7340.4840.1430.0000.0480.6511.0000.7840.3500.4100.2960.4520.4670.4610.4760.4410.460
cost0.3000.0420.9880.6260.0780.0000.0240.7380.7841.0000.2210.2570.2020.2660.2830.3260.2890.2960.272
style0.1390.0000.2070.0530.0280.0000.0030.2400.3500.2211.0000.2210.1030.2060.2430.2160.2710.2200.246
sizes0.1430.0250.2270.1050.0320.0000.0150.2380.4100.2570.2211.0000.3240.2890.3100.3060.3090.2930.275
gender0.0560.0220.1690.0970.0370.0000.0130.1760.2960.2020.1030.3241.0000.3150.2630.2220.2640.2330.199
rgb_r_main_col0.1580.0440.2330.1400.0710.0000.0220.2710.4520.2660.2060.2890.3151.0000.8110.8570.4400.4270.436
rgb_g_main_col0.1680.0250.2760.1800.0510.0000.0160.2440.4670.2830.2430.3100.2630.8111.0000.8120.4320.4800.440
rgb_b_main_col0.2320.0550.2770.1640.0800.0000.0260.2670.4610.3260.2160.3060.2220.8570.8121.0000.4140.4510.461
rgb_r_sec_col0.1150.0380.2730.1540.0730.0000.0050.2570.4760.2890.2710.3090.2640.4400.4320.4141.0000.8000.827
rgb_g_sec_col0.1520.0300.2700.1900.0670.0000.0190.2540.4410.2960.2200.2930.2330.4270.4800.4510.8001.0000.841
rgb_b_sec_col0.1300.0380.2760.1420.0750.0000.0130.2830.4600.2720.2460.2750.1990.4360.4400.4610.8270.8411.000
2023-08-12T23:11:05.251751image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
promo1sizesproductgrouppromo2countrystylegendercategory
promo11.0000.0000.0000.0530.0000.0000.0000.000
sizes0.0001.0000.1340.0110.0910.1430.1490.182
productgroup0.0000.1341.0000.0250.1330.1020.1140.360
promo20.0530.0110.0251.0000.1660.0060.0090.042
country0.0000.0910.1330.1661.0000.0420.0530.240
style0.0000.1430.1020.0060.0421.0000.0970.173
gender0.0000.1490.1140.0090.0530.0971.0000.163
category0.0000.1820.3600.0420.2400.1730.1631.000

Missing values

2023-08-12T23:10:46.391207image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-12T23:10:47.179274image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-12T23:10:48.140054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

countryarticlesalesregular_pricecurrent_priceratioretailweekpromo1promo2productgroupcategorycoststylesizesgenderrgb_r_main_colrgb_g_main_colrgb_b_main_colrgb_r_sec_colrgb_g_sec_colrgb_b_sec_col
0GermanyAA18216231.9525.950.8122072014-12-2810T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
1GermanyAA18212931.9522.950.7183102015-01-0400T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
2GermanyAA18215831.9528.950.9061032015-01-1100T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
3GermanyAA18214931.9529.950.9374022015-01-1800T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
4GermanyAA182188331.9524.950.7809082015-01-2500T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
5GermanyAA18214331.9531.951.0000002015-02-0100T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
6GermanyAA18216731.9528.950.9061032015-02-0800T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
7GermanyAA182117331.9515.950.4992182015-02-1500T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
8GermanyAA18215331.9522.950.7183102015-02-2200T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
9GermanyAA18211931.9526.950.8435052015-03-0100T-SHIRTSRUNNING3.2wides,m,l,xl,xxlunisex205.0133.063.079.079.079.0
countryarticlesalesregular_pricecurrent_priceratioretailweekpromo1promo2productgroupcategorycoststylesizesgenderrgb_r_main_colrgb_g_main_colrgb_b_main_colrgb_r_sec_colrgb_g_sec_colrgb_b_sec_col
81416GermanyZZ24661263.9534.950.5465212017-02-2600SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0
81417GermanyZZ2466363.9536.950.5777952017-03-0500SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0
81418GermanyZZ24662263.9529.950.4683352017-03-1200SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0
81419GermanyZZ24663663.9519.950.3119622017-03-1900SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0
81420GermanyZZ24664563.9524.950.3901492017-03-2600SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0
81421GermanyZZ246614763.9524.950.3901492017-04-0200SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0
81422GermanyZZ246625563.9519.950.3119622017-04-0900SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0
81423GermanyZZ24664563.9521.950.3432372017-04-1600SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0
81424GermanyZZ24661063.9519.950.3119622017-04-2300SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0
81425GermanyZZ246610963.9519.950.3119622017-04-3000SHOESTRAINING6.4regularxxs,xs,s,m,l,xl,xxlwomen174.0238.0238.0139.076.057.0